In the electric power distribution industry, there are many building models focusing on the thermal shell of a facility and the efficiencies of the installed equipment. These modeling approaches are based on a philosophy of heat gain and heat loss. These models require a great deal of effort to use and are more focused on overall energy usage than on energy demand. Modifying these building models for energy demand would be impractical for widespread use and would still require modification for behavior effects leading to diversities.
The owner of the invention is a large power company that supplies energy to approximately 4.2 million customers over a 120,000-square-mile service territory spanning several states. The power company includes four regulated retail electric utilities. Annually, the power company purchases approximately 70,000 distribution transformers.
When comparing the Kilovolt-Amp (kVA) demand loading on an electric power company's substations with the actual connected transformer nameplate kVA, it was found that, on average, the distribution transformers were not being heavily loaded. This finding was supported by historical evidence that a very small number of transformers fail due to overloads. Utilities could realize significant savings with an improved transformer size selection process for new facilities. Utilities could save in investment and operations by more closely sizing transformers to actual load, while still operating within acceptable risk and safety limits.
At the heart of this transformer size selection problem is a basic business issue of how to allocate an investment in distribution transformers based on balancing risk, value, and performance in an uncertain environment. Before a building to be served is built, and the actual load metered, the transformer size and design must be selected. To minimize risk and investment costs, each step of the process needed to be investigated and improved. The first and most crucial step in correctly sizing a transformer is to estimate the customer's future energy demand (kVA or watts). This includes the initial gathering of information and then applying the information to forecast the demand.